1. Field of the Invention
This invention relates to a dynamic system diagnosing apparatus, and a tire air pressure diagnosing apparatus and a vehicle body weight change detecting apparatus using the dynamic system diagnosing apparatus. More particularly the invention relates to a diagnosing apparatus for estimating a fault in terms of disturbance generated in a dynamic system to diagnose fault in a dynamic system, to detect an abnormality of a tire air pressure or to detect a change of a vehicle body weight, and also to a tire air pressure diagnosing apparatus and a vehicle body weight change detecting apparatus using such diagnosing apparatus.
2. Description of the Related Art
An apparatus for diagnosing a fault of a dynamic system is currently known. The diagnosing apparatus discriminates the presence/absence of the fault and the fault portion using a residual between the dynamic system's response estimated from a normal model of the dynamic system and the actual response measured by a sensor.
This conventional art is exemplified by "A Generalized Likelihood Ratio Approach to Detection and Estimation of Jumps in Linear Systems" by A.S. Willskey & H.L. Jones, IEEE Trans. AC-21, No. 1.
FIG. 7 shows a fault diagnosing apparatus 20 embodying this method. The fault diagnosing apparatus diagnoses a dynamic system 10 to be controlled based on a control input 14 from a controller 12. In FIG. 7, reference characters u and d designate a control input vector and an external disturbance vector, respectively, which are to be inputted to the dynamic system 10. The symbol y is a control output of the dynamic system 10, and x is an internal state vector of the dynamic system 10 measured using a sensor.
The fault diagnosing apparatus 20 comprises a normal model observer 22, a number of fault model observers 24-1, 24-2, . . . , 24-n, likelihood ratio detecting and estimating parts 26-1, 26-2, . . . , 26-n situated so as to correspond to the respective fault model observers, and a fault discriminating part 28.
The normal model observer 22 estimates the quantity of state of the dynamic system 10 based on the normal model from the control input vector u and the control output vector y of the dynamic system 10 and outputs an estimation output signal 23. A residual 25 between the estimation output signal 23 and the state vector x of the dynamic system 10, which is actually measured by a sensor, is inputted to the likelihood ratio detecting and estimating parts 26-1, 26-2, . . . , 26-n.
Each fault model observer 24-1, 24-2, . . . , 24-n estimates and calculates the state of the dynamic system 10 based on the respective different fault model. Residuals 29-1, 29-2, . . . , 29-n between estimation outputs 27-1, 27-2, . . . , 27-n of the respective fault model observer 24-1, 24-2, . . . , 24-n and state x of the dynamic system 10 actually measured are inputted to the corresponding likelihood ratio detecting and estimating parts 26-1, 26-2, . . . , 26-n.
Each likelihood ratio detecting and estimating part 26-1, 26-2, . . . , 26-n calculates a probability (likelihood ratio) of the corresponding model matching with the present dynamic system 10, from the residual signals 25 and 29 from the normal model observer 22 and the fault model observer 24, respectively. The result of this calculation will be outputted to the fault discriminating part 28.
Thus each likelihood ratio detecting and estimating part 26-1, 26-2, . . . , 26-n calculates the likelihood ratio of the respective model matching with the present dynamic system 10, for every assumed fault model. The fault discriminating part 28 determines the fault model of the maximum likelihood ratio from the input signal, thereby discriminating the occurence of a fault of the dynamic system 10 and a fault portion.
However, the conventional apparatus 20 has the following problems:
Firstly, this conventional apparatus 20 obtains a model which corresponds to the fault, from the residuals 25, 29 between the states 23, 27 estimated by the observers 22, 24 and the actually measured value x. This residual is considerably dependent on the design of the observer; the higher the rate of detecting the state of the observer (fault detecting rate), the smaller the residual so that the fault detection sensitivity will be lowered.
In a noisy system in particular, only a large and sudden fault can be detected.
With the conventional apparatus, it is complex in calculating the likelihood ratio for fault detection. Besides, this calculation must take place for each fault model. Consequently the calculation quantity would be extremely so that the conventional apparatus could not cope with diagnosis in real time.
Further, if even a single quantity of internal state x of the dynamic system 10 to be diagnosed could not be measured, it would be impossible to detect and specify the internal fault of the diagnosing object. Namely, the quantity of internal state x of the dynamic system 10 is detected as a state vector composed of a number of elements. Therefore, if even a single element of the state vector x could be be measured, it would be impossible to detect or specify the internal fault of the diagnosing object.
Furthermore, the conventional apparatus does not utilize the concept of separating an external disturbance d, which is to penetrate from an external source, from an internal disturbance, which is generated due to the internal fault. Therefore the measuring accuracy tends to be influenced by the external disturbance.